Maxautofactors

Purpose

Maximum / Principal Autocorrelation Factors.

Synopsis

[model] = maxautofactors(x,ncomp,options)

Description

In it's default mode, MAXAUTOFACTORS uses a generalized eigenvalue decomposition to provide a model of data (x) which captures maximum spatially-correlated variance. An approximate solution is used to stabelize and speed up the algorithm (see options.varcap).

Inputs

• x = MxNxP image class 'dataset' or 'double'.
• ncomp = number of components (integer).

Outputs

• model: standard model structure containing the MAF model (see MODELSTRUCT).
• options: options structure. (some fields may have been modified)

Options

options = a structure array with the following fields:

• display: [ 'off' | {'on'} ] governs level of display to command window.
• plots: [ 'none' | {'final'} ] governs level of plotting.
• algorithm: [ {'maf'} | 'paf' | 'mdf' | 'pdf' ]

if algorithm == 'maf' or 'paf' the options settings are for numerator and denomenator operators to be I and the first difference respectively.

if algorithm == 'mdf' or 'pdf' the options settings are for numerator and denomenator operators to be 1stD and the 2ndD respectively.

• varcap: [{0.999}] 0<varcap<1, specifies the variance of X to be captured when approximating the input X with a PCA model.

If (varcap) is an integer >=ncomp, this is the number of PCs used. The minimum number is (ncomp).

• smooth: [ ] smoothness penalty, based on the fraction of variance of the numerator (typical value might be 1e-3 to 0.05).

Smoothness is only available for MAF and MDF.

See Also

mcr, parafac, pca